Background understanding in video data

ABSTRACT

Long-term understanding of background modeling includes determining first and second dimension gradient model derivatives of image brightness data of an image pixel along respective dimensions of two-dimensional, single channel image brightness data of a static image scene. The determined gradients are averaged with previous determined gradients of the image pixels, and with gradients of neighboring pixels as a function of their respective distances to the image pixel, the averaging generating averaged pixel gradient models for each of a plurality of pixels of the video image data of the static image scene that each have mean values and weight values. Background models for the static image scene are constructed as a function of the averaged pixel gradients and weights, wherein the background model pixels are represented by averaged pixel gradient models having similar orientation and magnitude and weights meeting a threshold weight requirement.

BACKGROUND

Background understanding in video analytics applications refers tomodeling the background of video image data, the static (non-moving)part of a static camera field-of-view that defines or otherwise belongsto the environmental settings of the image. For example, the road thatmoving objects travel upon, a building that moving pedestrians enter andexit, etc. By knowing the background and performing a differenceoperation between current video frame images with the background, movingforeground objects may be detected and identified, enabling other videoanalytics operations appropriate for the detected object, such as movingobject tracking, classification, activity significance recognition anddetermination, etc.

BRIEF SUMMARY

In one embodiment of the present invention, a method for long-termunderstanding of background modeling includes determining a firstdimension gradient model derivative of image brightness data of an imagepixel along a first dimension of two-dimensional image data, and asecond dimension gradient model derivative of the image brightness dataof the image pixel along the second dimension of the two-dimensionalimage data, wherein the image brightness data is single channel of colorinformation extracted from video image data of a static image scene. Aprocessing unit averages the determined gradients with any previousdetermined gradients of the image pixel, and with gradients of each of aplurality of neighboring pixels as a function of the respectivedistances of the neighboring pixels to the image pixel, the averaginggenerating averaged pixel gradient models for each of a plurality ofpixels of the video image data of the static image scene that each havemean values and weight values. Thus, the processing unit constructsbackground models for the static image scene as a function of theaveraged pixel gradient model mean values and weight values, whereineach pixel in the background model is represented by a set of theaveraged pixel gradient models that each have similar orientation andmagnitude and have a weight meeting a background threshold weightrequirement.

In another embodiment, a method for providing a service for long-termunderstanding of background modeling includes providing one or morearticles, including a gradient determiner that uses features extractedfrom input video data to determine dimensional gradient models for pixelimage data of the input video, and defines average image pixel gradientmodels for each pixel of the pixel image data by averaging thedetermined gradients with previous gradients of the each pixel, and alsowith gradients of neighboring pixels as a function of their distance tothe pixel. A background modeler is provided that constructs and updatesbackground pixel models for a static image scene of the input video databy using the averaged pixel gradient data that have similar orientationand magnitude for each of a plurality of pixel model sets, wherein eachpixel model set is associated with a weight determinative as to whethereach pixel model set represents background or non-background pixel data.A foreground estimator is also provided that uses the constructed andupdated background models to estimate foreground areas in the staticimage scene of the input video data by comparing the averaged pixelgradients with corresponding ones of the background models, wherein apixel is determined to be a foreground and not a background pixel if adistance from the averaged pixel gradient data to a correspondingbackground model pixel set is equal to or above a threshold distancevalue, or if the distance is less than the threshold but it is closer toanother foreground model pixel set than to the corresponding backgroundmodel pixel set.

In another embodiment, a system has a processing unit, computer readablememory and a computer readable storage medium device with programinstructions, wherein the processing unit, when executing the storedprogram instructions, determines a first dimension gradient modelderivative of image brightness data of an image pixel along a firstdimension of two-dimensional image data, and a second dimension gradientmodel derivative of the image brightness data of the image pixel alongthe second dimension of the two-dimensional image data, wherein theimage brightness data is single channel of color information extractedfrom video image data of a static image scene. The processing unitfurther averages the determined gradients with any previous determinedgradients of the image pixel, and with gradients of each of a pluralityof neighboring pixels as a function of the respective distances of theneighboring pixels to the image pixel, the averaging generating averagedpixel gradient models for each of a plurality of pixels of the videoimage data of the static image scene that each have mean values andweight values. Thus, the processing unit constructs background modelsfor the static image scene as a function of the averaged pixel gradientmodel mean values and weight values, wherein each pixel in thebackground model is represented by a set of the averaged pixel gradientmodels that each have similar orientation and magnitude and have aweight meeting a background threshold weight requirement.

In another embodiment, an article of manufacture has a computer readablestorage medium device with computer readable program code embodiedtherewith, the computer readable program code comprising instructionsthat, when executed by a computer processor, cause the computerprocessor to determine a first dimension gradient model derivative ofimage brightness data of an image pixel along a first dimension oftwo-dimensional image data, and a second dimension gradient modelderivative of the image brightness data of the image pixel along thesecond dimension of the two-dimensional image data, wherein the imagebrightness data is single channel of color information extracted fromvideo image data of a static image scene. A computer processor isfurther caused to average the determined gradients with any previousdetermined gradients of the image pixel, and with gradients of each of aplurality of neighboring pixels as a function of the respectivedistances of the neighboring pixels to the image pixel, the averaginggenerating averaged pixel gradient models for each of a plurality ofpixels of the video image data of the static image scene that each havemean values and weight values. Thus, the computer processor constructsbackground models for the static image scene as a function of theaveraged pixel gradient model mean values and weight values, whereineach pixel in the background model is represented by a set of theaveraged pixel gradient models that each have similar orientation andmagnitude and have a weight meeting a background threshold weightrequirement.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

These and other features of this invention will be more readilyunderstood from the following detailed description of the variousaspects of the invention taken in conjunction with the accompanyingdrawings in which:

FIG. 1 is a block diagram illustration of an embodiment of a method,process or system for automated long-term understanding of backgroundmodeling according to the present invention.

FIG. 2 is a block diagram illustration of an embodiment of a method,process or system for feature extraction and average image pixelgradient determination according to the present invention.

FIG. 3 is a diagrammatic illustration of dimension Gaussian filterderivatives for use with an embodiment of the present invention.

FIG. 4 is a block diagram illustration of an embodiment of a method,process or system of the present invention.

FIG. 5 is a block diagram illustration of an embodiment of a method,process or system for constructing background models.

FIG. 6 is a block diagram illustration of an embodiment of a method,process or system of the present invention.

FIG. 7 is a block diagram illustration of a computerized implementationof an embodiment of the present invention.

FIG. 8 is a block diagram illustration of an article according to thepresent invention.

The drawings are not necessarily to scale. The drawings are merelyschematic representations, not intended to portray specific parametersof the invention. The drawings are intended to depict only typicalembodiments of the invention, and therefore should not be considered aslimiting the scope of the invention. In the drawings, like numberingrepresents like elements.

DETAILED DESCRIPTION

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, in abaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including, but not limited to, wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

A variety of approaches are used for background modeling in computervision, multimedia signal processing, robotics, and other videoanalytics applications. Examples include feature perspective processesbased on analysis of raw pixel colors in Red-Green-Blue (RGB) values orHue, Saturation, and Value (HSV) image planes, and textures perspectiveprocesses that consider local binary patterns, edges, etc. However, rawcolor approaches do not handle lighting changes well. Additionally, rawcolor and texture process also generally implement adaptation techniqueswherein background models tend to adapt to more recent changes in theimage scene different than changes determined over longer time periods.More particularly, background models constructed using parametricformulations such as Gaussian mixture models, or through non-parametricapproaches such as kernel density estimation, typically assign higherweights for more recently obtained pixel samples. As a result, suchsystems tend to adapt their models in response to the most recent staticimage information, the background models effectively forgetting previousscene object contents over time.

FIG. 1 illustrates a method, process or system for long-termunderstanding of background modeling. At 102 features extracted frominput video data 101 are used to determine dimensional gradient modelsfor the image data pixels, first-order derivatives of the image pixelbrightness along each of the dimensions of the image; thus, fortwo-dimensional image data, along horizontal (x) and vertical (y)dimensions. The determined gradients are used to define average imagepixel gradient models 103 for the pixels by averaging the determinedgradients with all previous gradients of the pixel, and also withgradients of neighboring pixels as a function of their distance to thepixel. At 104 background models 105 are constructed and updated usingthe averaged pixel gradient data 103 as input, wherein each pixel isrepresented by a set of gradient models, via means of gradient sampleshaving similar orientation and magnitude, and wherein each model set isalso associated with a weight which determines if the pixel representsbackground or non-background data, the weighting independent of time ofthe pixel data.

At 106 the constructed background models 105 are used to estimateforeground areas in the image by comparing the averaged pixel gradients103 with the corresponding background models 105. More particularly, apixel is determined to be a foreground (non-background) pixel at 106 if:(a) the distance from the pixel's gradient to the background model isequal to or above a threshold distance value, or (b) if the distance isless than the threshold, but it is closer to another foreground modelthan to the background model. The results of the foreground andbackground determinations at 106 are processed at 108, and theforeground pixels used to define a foreground mask 109 representing aforeground object.

FIG. 2 illustrates one embodiment of a process for feature extractionand average image pixel gradient determination at 102 of FIG. 1. If theinput video data 101 is color image data (i.e., having more than onechannel of color information) then at 202 the image data is converted toa grey-scale image, such that only one channel is maintained for theimage data. At 204 the image data is convolved with dimension-specificgradient filters for each dimension of the image data. In the presentembodiment, the image data is two-dimensional data, and thus convolvingcomprises using one filter for the horizontal (x) dimension and anotherone for vertical (y) dimension, to generate image pixel gradients foreach dimension, which may be notated as (p_x, p_y). (Other embodimentsfor use with other dimensions would use additional filters for eachdimension, thus three dimensional imaging would use three filters, etc.)The present example uses derivatives of Gaussian filters for eachdimension, for example the horizontal (x) Gaussian filter derivative 302and the vertical (y) Gaussian filter derivative 304 illustrated in FIG.3 though, as will be appreciated by one skilled in the art, a variety ofother filters may also be used, such as triangular filters, sinusoidfilters, etc. FIG. 4 illustrates one example of the process, wherein aninput color image 402 from the input data 101 is converted at 202 into agrey-scale image 402, which is then convolved at 204 to generate ahorizontal dimension pixel gradient (Fx) 406 and a vertical dimensionpixel gradient (Fy) 408.

At 206 pixel gradients are refined as a function of determining averageor weighted gradient mean values of the respective vertical andhorizontal gradient values with the corresponding vertical andhorizontal gradient values of the pixels within the neighborhood of theimage pixel to generate the average pixel gradients 103. In one aspect,by using the average pixel gradients 103 as basis data for generatingbackground models, rather than using individual pixel gradient dataindependent of considering the context of neighborhood pixels as taughtby the prior art, the background models generated by embodiments of thepresent invention are found to be robust against minor object motionswithin the image data, for example against minor motions caused bywaving tree branches and camera shakes.

FIG. 5 illustrates one embodiment of the process for constructingbackground models at 104. In response to an average pixel gradient 103input, the process determines at 502 if there is an existing model forthat pixel. If not, then at 504, a first model is initiated for theinput pixel using its current average pixel gradient data as the modelmean, and with an initial weight of one. The initial weight may also bean empirically determined value. In one example, the pixel models may berepresented by M_(i)(u, w), where “u” is the mean, and “w” is theweight, and the input pixel gradient may be designated F=(f_(x), f_(y)).

If instead it is determined at 502 that there are existing models forthe pixel, then at 506 distances are determined between the new inputpixel gradient F and the means of each of the existing models M to finda model M_(O) having the smallest distance D(F, M_(O)). The presentembodiment use a Euclidean distance D, though it will be appreciatedthat a variety of ways may be used to compute such a distance, forexample a Normal kernal process, and still others will be apparent toone skilled in the art. At 508 the smallest model distance D(F, M_(O))is compared to a threshold matching value. If the distance D(F, M_(O))is greater than this threshold at 508, then at 504 a new model isinitiated for the input pixel using its average pixel gradient data asthe model mean, and again with an initial weight of one.

Otherwise, if the distance D(F, M_(O)) meets the matching thresholdrequirements at 508, then the existing model M_(O) is updated at 510 and512. More particularly, the mean (u) of the model M_(O) is updated at512 with an absolute average determined by: u_(T+1)=(u_(T)*w+F)/(w+1),wherein “w” is the total number of model samples taken at differenttimes (T). By using the absolute average to update the mean the processat 104 ensures that each of the samples obtained will have equal weightsin the model construction process, regardless of the time that thesample came in. Thus, the present embodiment avoids adaptation problemsprevalent in prior art approaches that assume fixed weights for morerecent samples.

The weight of the target model M_(O) is updated at 510 by incrementingthe weight by one, and a kernel density estimation technique assignsweights to neighboring models. More particularly, the new weight for anynon-matching model is updated with a factor defined by a decaying kernelfunction (K(D(F, M)), where D is the distance between the new gradientand the target model, and wherein the maximum of decaying kernelfunction is at the new gradient value and it decreases in alldirections. The present example uses a Gaussian decaying kernelfunction, though any of a variety of decaying kernel functions may beused, such as linear kernels, log kernels, etc. In one aspect, thepresent embodiment differs from prior art approaches that implement arule of exclusion wherein only the weight of the matched model isincremented while weights of other models remain the same. Such priorart approaches do not adequately handle edge cases where the new sampleis at middle range between two or more models; even though a matchingmodel can be found in this case, the other nearby model should not beomitted from consideration.

At 512 the background models are updated in response to the first or newmodels created at 504 and/or the target model M_(O) updated at 510 and512, wherein the models with the largest weights representing thebackground and the other models represent non-background information(e.g., moving objects). The embodiment may also place the model with thelargest weight in the beginning, in one aspect to avoid sorting costs ateach round or iteration of model update. Additionally, if two modelshave the same weights, then the one with the latest in time observationsmay be considered as the background model.

FIG. 4 illustrates a horizontal (x) background model 416 and a vertical(y) background model 418 generated from the respective horizontaldimension pixel gradient (Fx) 406 and a vertical dimension pixelgradient (Fy) 408. FIG. 6 shows an example foreground detection result608 wherein a foreground object mask 609 is generated in response to anon-static object (automobile) 601 in an input image 602 from theresults 624 and 626 matching a horizontal dimension pixel gradient (Fx)604 and a vertical dimension pixel gradient (Fy) 606 to respectivehorizontal (x) and vertical (y) background models 614, 616, as describedabove with respect to FIGS. 1-5.

Thus, embodiments of the present invention represent each pixel by a setof feature clusters derived from the dimensional gradients of that pixelover time. The feature clusters are collected and constructed byassigning equal weights to all pixel samples, independent of timeconsiderations: the resultant background model has the same memory forsamples obtained recently as for those obtained in the past. Thus, it ismore robust against the incorrect adaptation problem. In another aspect,the number of the gradient models for a given image pixel is generallyvariable, in one aspect as relatively stable areas need fewer modelinputs to update the background, but while areas relatively rich withmotion need more model data for accurate updating. The embodiment isalso more robust against lighting changes over time than prior artbackground modeling techniques that rely upon color information.

Embodiments of the present invention thus provide solutions forseparating foreground objects from background in videos acquired bystationary cameras where the true background is constant in the selectedfeature space. Pixel gradients are smoothed (averaged) in the spatialdomain through considering the average of neighborhood gradients, ratherthan computing the temporal average of the gradient of only matchedpixels. Averaging is not limited to only those pixels at the same imagelocation, as is taught by the prior art; such prior art limitations mayprovide a temporal smoothness of a same object over time, but withoutproviding spatial smoothness around its neighborhood. Embodiments of thepresent invention temporally cluster the gradients into pixel models,which are the clusters of similar gradients of the same pixels overtime. Embodiments of the present invention also combine both parametricrepresentations of the models (via the means of the gradients) with akernel density estimation technique.

Referring now to FIG. 7, an exemplary computerized implementation of anembodiment of the present invention includes a computer or otherprogrammable device 522 in communication with one or more cameras orother sources 540 of the input video data 101. Instructions 542 residewithin computer readable code in a computer readable memory 536, or in acomputer readable storage system 532, or other tangible computerreadable storage medium that is accessed through a computer networkinfrastructure 526 by a processing unit (CPU) 538. Thus, theinstructions, when implemented by the processing unit (CPU) 538, causethe processing unit (CPU) 538 to perform long-term understanding ofbackground modeling as described above with respect to FIG. 1, and insome embodiments of the present invention also with respect to one ormore of FIGS. 2-6.

FIG. 8 illustrates an embodiment of an article 551 (for example, aprogrammable device, system, etc.) according to the present inventionthat performs an automated long-term understanding of backgroundmodeling as described above with respect to FIG. 1, and in someembodiments of the present invention also with respect to one or more ofFIGS. 2-7. One or more of the components of the article 551 are tangibledevices that perform specific functions, for example comprising theprocessing unit 538, computer readable memory 516 and tangible computerreadable storage medium 532 of FIG. 7. More particularly, a GradientDeterminer 552 uses features extracted from the input video data 101 todetermine dimensional gradient models for the image data pixels, anddefines the average image pixel gradient models 103 for pixels byaveraging the determined gradients with previous gradients of the pixeland also with gradients of neighboring pixels as a function of theirdistance to the pixel. A Background Modeler constructs and updates thebackground models 105 using the averaged pixel gradient data 103 viameans of gradient samples having similar orientation and magnitude, andwherein each model set is also associated with a weight which determinesif the pixel represents background or non-background data. A ForegroundEstimator 556 uses the constructed background models 105 to estimateforeground areas in image data by comparing the averaged pixel gradients103 with the corresponding background models 105, wherein a pixel isdetermined to be a foreground (non-background) pixel if the distancefrom the pixel's gradient to the background model is equal to or above athreshold distance value, or if the distance is less than the threshold,but it is closer to another foreground model than to the backgroundmodel. The output of the Foreground Estimator 556 is used by aForeground Mask Determiner to define a foreground masks representingforeground objects.

Embodiments of the present invention may also perform process steps ofthe invention on a subscription, advertising, and/or fee basis. That is,a service provider could offer to perform automated long-termunderstanding of background modeling as described above with respect toFIGS. 1-8. Thus, the service provider can create, maintain, and support,etc., a computer infrastructure such as the computer system 522, networkenvironment 526, or parts thereof, or the article 551, that perform theprocess steps of the invention for one or more customers. In return, theservice provider can receive payment from the customer(s) under asubscription and/or fee agreement and/or the service provider canreceive payment from the sale of advertising content to one or morethird parties. Services may comprise one or more of: (1) installingprogram code on a computing device, such as the computers/devices522/551, from a tangible computer-readable medium device 520 or 532; (2)adding one or more computing devices to a computer infrastructure; and(3) incorporating and/or modifying one or more existing systems of thecomputer infrastructure to enable the computer infrastructure to performthe process steps of the invention.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. Certain examples and elementsdescribed in the present specification, including in the claims and asillustrated in the Figures, may be distinguished or otherwise identifiedfrom others by unique adjectives (e.g. a “first” element distinguishedfrom another “second” or “third” of a plurality of elements, a “primary”distinguished from a “secondary” one or “another” item, etc.) Suchidentifying adjectives are generally used to reduce confusion oruncertainty, and are not to be construed to limit the claims to anyspecific illustrated element or embodiment, or to imply any precedence,ordering or ranking of any claim elements, limitations or process steps.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A computer-implemented method for backgroundmodeling, the method comprising executing on a processor the steps of:determining dimensional gradients for pixel image data extracted frominput video data; defining average image pixel gradient models for eachpixel of the pixel image data by averaging the determined dimensionalgradients with at least one of previous dimensional gradients of theeach pixel, and dimensional gradients of neighboring pixels as afunction of their distance to the pixel; and constructing and updatingbackground pixel models for a static image scene of the input video databy using data of the averaged pixel dimensional gradient data that hassimilar orientation and magnitude for each of a plurality of pixel modelsets.
 2. The method of claim 1, further comprising: integratingcomputer-readable program code into a computer system comprising theprocessor, a computer readable memory in circuit communication with theprocessor, and a computer readable storage medium in circuitcommunication with the processor; and wherein the processor executesprogram code instructions stored on the computer-readable storage mediumvia the computer readable memory and thereby performs the steps ofdetermining the dimensional gradients for the pixel image data extractedfrom the input video data, defining the average image pixel gradientmodels for each pixel of the pixel image data by averaging thedetermined dimensional gradients with the at least one of the previousdimensional gradients of the each pixel and the dimensional gradients ofthe neighboring pixels, and constructing and updating the backgroundpixel models for the static image scene of the input video data by usingthe data of the averaged pixel dimensional gradient data that has thesimilar orientation and magnitude for each of the plurality of pixelmodel sets.
 3. The method of claim 1, wherein each of the plurality ofpixel model sets is associated with a respective weight that isdeterminative of whether the each pixel model set represents backgroundor non-background data.
 4. The method of claim 3, further comprising:using the constructed and updated background models to estimateforeground areas in the static image scene of the input video data bycomparing the averaged pixel gradients with corresponding ones of thebackground models, wherein a pixel is determined to be a foreground andnot a background pixel in response to at least one of a distance fromthe averaged pixel gradient data to a corresponding background modelpixel set meeting a threshold distance value, and to the distance beingless than the threshold distance value but closer to another foregroundmodel pixel set than to the corresponding background model pixel set. 5.The method of claim 4, further comprising determining and averaging thedetermined gradients by: convolving extracted image feature data of theimage pixel with a horizontal filter for a horizontal dimension of thestatic image to generate a horizontal dimension pixel gradient, and witha vertical filter for a vertical dimension of the static image, togenerate a vertical dimension pixel gradient; averaging the horizontaldimension pixel gradient with horizontal dimension pixel gradients ofthe any previous determined gradients of the image pixel, and withhorizontal dimension pixel gradients of the each of the plurality ofneighboring pixels as a function of the respective distances of theneighboring pixels to the image pixel; and averaging the verticaldimension pixel gradient with vertical dimension pixel gradients of theany previous determined gradients of the image pixel, and with verticaldimension pixel gradients of the each of the plurality of neighboringpixels as a function of the respective distances of the neighboringpixels to the image pixel.
 6. The method of claim 4, further comprisingconstructing and updating the background pixel models by: determining ifthere is at least one existing pixel model for an image pixel; inresponse to determining that there is not an existing model, initiatinga first model for the image pixel comprising the image pixel averagedgradient mean value as a first model mean, and an initial weight of one;in response to determining that there is at least one existing model,determining distances between the image pixel averaged gradient meanvalue and the means of each of the at least one existing models toidentify a model having the smallest model distance, and updating themean of identified existing model to an absolute average mean as afunction of the image pixel averaged gradient mean value andincrementing a weight of the identified model in response to thesmallest model distance meeting a matching threshold requirement;updating weights of models neighboring the image pixel with a factordefined by a decaying kernel function as a function of their distance tothe image pixel; and updating background models in response to the firstor updated models, wherein pixel models with largest weights representthe background and other pixel models without the largest weightsrepresent foreground data.
 7. The method of claim 6, further comprising:updating the background models by, in response to two models having thesame weights, considering the one of the two models with a later in timeobservation as a background model pixel.
 8. The method of claim 7,further comprising: converting the color image data to grey-scale imagebrightness data for the extracted features image brightness data inresponse to the input video data color image data comprising more thanone channel of color information.
 9. A system, comprising: a processingunit; a computer readable memory in communication with the processingunit; and a computer-readable tangible storage medium in communicationwith the processing unit; wherein the processing unit executes programinstructions stored on the computer-readable tangible storage medium viathe computer readable memory and thereby: determines dimensionalgradients for pixel image data extracted from input video data; definesaverage image pixel gradient models for each pixel of the pixel imagedata by averaging the determined dimensional gradients with at least oneof previous dimensional gradients of the each pixel, and dimensionalgradients of neighboring pixels as a function of their distance to thepixel; and constructs and updates background pixel models for a staticimage scene of the input video data by using data of the averaged pixeldimensional gradient data that has similar orientation and magnitude foreach of a plurality of pixel model sets.
 10. The system of claim 9,wherein each of the plurality of pixel model sets is associated with arespective weight that is determinative of whether the each pixel modelset represents background or non-background data.
 11. The system ofclaim 10, wherein the processing unit executes the program instructionsstored on the computer-readable storage medium via the computer readablememory, and thereby: uses the constructed and updated background modelsto estimate foreground areas in the static image scene of the inputvideo data by comparing the averaged pixel gradients with correspondingones of the background models, wherein a pixel is determined to be aforeground and not a background pixel in response to at least one of adistance from the averaged pixel gradient data to a correspondingbackground model pixel set meeting a threshold distance value, and tothe distance being less than the threshold distance value but closer toanother foreground model pixel set than to the corresponding backgroundmodel pixel set.
 12. The system of claim 11, wherein the processing unitexecutes the program instructions stored on the computer-readablestorage medium via the computer readable memory, and thereby determinesand averages the determined gradients by: convolving extracted imagefeature data of the image pixel with a horizontal filter for ahorizontal dimension of the static image to generate a horizontaldimension pixel gradient, and with a vertical filter for a verticaldimension of the static image, to generate a vertical dimension pixelgradient; averaging the horizontal dimension pixel gradient withhorizontal dimension pixel gradients of the any previous determinedgradients of the image pixel, and with horizontal dimension pixelgradients of the each of the plurality of neighboring pixels as afunction of the respective distances of the neighboring pixels to theimage pixel; and averaging the vertical dimension pixel gradient withvertical dimension pixel gradients of the any previous determinedgradients of the image pixel, and with vertical dimension pixelgradients of the each of the plurality of neighboring pixels as afunction of the respective distances of the neighboring pixels to theimage pixel.
 13. The system of claim 11, wherein the processing unitexecutes the program instructions stored on the computer-readablestorage medium via the computer readable memory, and thereby constructsand updates the background pixel models by: determining if there is atleast one existing pixel model for an image pixel; in response todetermining that there is not an existing model, initiating a firstmodel for the image pixel comprising the image pixel averaged gradientmean value as a first model mean, and an initial weight of one; inresponse to determining that there is at least one existing model,determining distances between the image pixel averaged gradient meanvalue and the means of each of the at least one existing models toidentify a model having the smallest model distance, and updating themean of identified existing model to an absolute average mean as afunction of the image pixel averaged gradient mean value andincrementing a weight of the identified model in response to thesmallest model distance meeting a matching threshold requirement;updating weights of models neighboring the image pixel with a factordefined by a decaying kernel function as a function of their distance tothe image pixel; and updating background models in response to the firstor updated models, wherein pixel models with largest weights representthe background and other pixel models without the largest weightsrepresent foreground data.
 14. The system of claim 13, wherein theprocessing unit executes the program instructions stored on thecomputer-readable storage medium via the computer readable memory, andthereby: updates the background models by, in response to two modelshaving the same weights, considering the one of the two models with alater in time observation as a background model pixel.
 15. A computerprogram product for background modeling, the computer program productcomprising: a computer readable storage medium having computer readableprogram code embodied therewith, wherein the computer readable storagemedium is not a transitory signal per se, the computer readable programcode comprising instructions for execution by a processor that cause theprocessor to: determine dimensional gradients for pixel image dataextracted from input video data; define average image pixel gradientmodels for each pixel of the pixel image data by averaging thedetermined dimensional gradients with at least one of previousdimensional gradients of the each pixel, and dimensional gradients ofneighboring pixels as a function of their distance to the pixel; andconstruct and update background pixel models for a static image scene ofthe input video data by using data of the averaged pixel dimensionalgradient data that has similar orientation and magnitude for each of aplurality of pixel model sets.
 16. The computer program product of claim15, wherein each of the plurality of pixel model sets is associated witha respective weight that is determinative of whether the each pixelmodel set represents background or non-background data.
 17. The computerprogram product of claim 16, wherein the computer readable program codeinstructions for execution by the processor further cause the processorto: use the constructed and updated background models to estimateforeground areas in the static image scene of the input video data bycomparing the averaged pixel gradients with corresponding ones of thebackground models, wherein a pixel is determined to be a foreground andnot a background pixel in response to at least one of a distance fromthe averaged pixel gradient data to a corresponding background modelpixel set meeting a threshold distance value, and to the distance beingless than the threshold distance value but closer to another foregroundmodel pixel set than to the corresponding background model pixel set.18. The computer program product of claim 17, wherein the computerreadable program code instructions for execution by the processorfurther cause the processor to determine and average the determinedgradients by: convolving extracted image feature data of the image pixelwith a horizontal filter for a horizontal dimension of the static imageto generate a horizontal dimension pixel gradient, and with a verticalfilter for a vertical dimension of the static image, to generate avertical dimension pixel gradient; averaging the horizontal dimensionpixel gradient with horizontal dimension pixel gradients of the anyprevious determined gradients of the image pixel, and with horizontaldimension pixel gradients of the each of the plurality of neighboringpixels as a function of the respective distances of the neighboringpixels to the image pixel; and averaging the vertical dimension pixelgradient with vertical dimension pixel gradients of the any previousdetermined gradients of the image pixel, and with vertical dimensionpixel gradients of the each of the plurality of neighboring pixels as afunction of the respective distances of the neighboring pixels to theimage pixel.
 19. The computer program product of claim 18, wherein thecomputer readable program code instructions for execution by theprocessor further cause the processor to construct and update thebackground pixel models by: determining if there is at least oneexisting pixel model for an image pixel; in response to determining thatthere is not an existing model, initiating a first model for the imagepixel comprising the image pixel averaged gradient mean value as a firstmodel mean, and an initial weight of one; in response to determiningthat there is at least one existing model, determining distances betweenthe image pixel averaged gradient mean value and the means of each ofthe at least one existing models to identify a model having the smallestmodel distance, and updating the mean of identified existing model to anabsolute average mean as a function of the image pixel averaged gradientmean value and incrementing a weight of the identified model in responseto the smallest model distance meeting a matching threshold requirement;updating weights of models neighboring the image pixel with a factordefined by a decaying kernel function as a function of their distance tothe image pixel; and updating background models in response to the firstor updated models, wherein pixel models with largest weights representthe background and other pixel models without the largest weightsrepresent foreground data.
 20. The computer program product of claim 19,wherein the computer readable program code instructions for execution bythe processor further cause the processor to: update the backgroundmodels by, in response to two models having the same weights,considering the one of the two models with a later in time observationas a background model pixel.